An original framework for understanding human actions and body language by using deep neural networks

The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour. By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way. These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements; both are essential tasks in many computer vision applications, including event recognition, and video surveillance. In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided. The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements. All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods

Learning from human-robot interaction

En los últimos años cada vez es más frecuente ver robots en los hogares. La robótica está cada vez más presente en muchos aspectos de nuestras vidas diarias, en aparatos de asistencia doméstica, coches autónomos o asistentes personales. La interacción entre estos robots asistentes y los usuarios es uno de los aspectos clave en la robótica de servicio. Esta interacción necesita ser cómoda e intuitiva para que sea efectiva su utilización. Estas interacciones con los usuarios son necesarias para que el robot aprenda y actualice de manera natural tanto su modelo del mundo como sus capacidades. Dentro de los sistemas roboticos de servicio, hay muchos componentes que son necesarios para su buen funcionamiento. Esta tesis esta centrada en el sistema de percepción visual de dichos sistemas.Para los humanos la percepción visual es uno de los componentes más esenciales, permitiendo tareas como reconocimiento de objetos u otras personas, o estimación de información 3D. Los grandes logros obtenidos en los últimos años en tareas de reconocimiento automático utilizan los enfoques basados en aprendizaje automático, en particular técnicas de deep learning. La mayoría de estos trabajos actuales se centran en modelos entrenados 'a priori' en un conjunto de datos muy grandes. Sin embargo, estos modelos, aunque entrenados en una gran cantidad de datos, no pueden, en general, hacer frente a los retos que aparecen al tratar con datos reales en entornos domésticos. Por ejemplo, es frecuente que se de el caso de tener nuevos objetos que no existían durante el entrenamiento de los modelos. Otro reto viene de la dispersión de los objetos, teniendo objetos que aparecen muy raramente y por lo tanto habia muy pocos, o ningún, ejemplos en los datos de entenamiento disponibles al crear el modelo.Esta tesis se ha desarrollado dentro del contexto del proyecto IGLU (Interactive Grounded Language Understanding). Dentro del proyecto y sus objetivos, el objetivo principal de esta Tesis doctoral es investigar métodos novedosos para que un robot aprenda de manera incremental mediante la interacción multimodal con el usuario.Desarrollando dicho objetivo principal, los principales trabajos desarrollados durante esta tesis han sido:-Crear un benchmark más adecuado para las tareas de aprendizaje mediante la interacción natural de usuario y robot. Por ejemplo, la mayoría de los datasets para la tarea de reconocimiento de objetos se centra en fotos de diferentes escenarios con múltiples clases por foto. Es necesario un dataset que combine interacción usuario robot con aprendizaje de objetos.-Mejorar sistemas existentes de aprendizaje de objetos y adecuarlos para aprendizaje desde la interacción multimodal humana. Los trabajos de detección de objetos se focalizan en detectar todos los objetos aprendidos en una imagen. Nuestro objetivo es usar la interacción para encontrar el objeto de referencia y aprenderlo incrementalmente.-Desarrollar métodos de aprendizaje incremental que se puedan utilizar en escenarios incrementales, p.e., la aparición de una nueva clase de objeto o cambios a lo largo del tiempo dentro de una clase objetos. Nuestro objetivo es diseñar un sistema que pueda aprender clases desde cero y que pueda actualizar los datos cuando estos aparecen.-Crear un completo prototipo para el aprendizaje incremental y multimodal usando la interacción humana-robot. Se necesita realizar la integración de los distintos métodos desarrollados como parte de los otros objetivos y evaluarlo.<br /

Machine learning approaches to video activity recognition: from computer vision to signal processing

244 p.La investigación presentada se centra en técnicas de clasificación para dos tareas diferentes, aunque relacionadas, de tal forma que la segunda puede ser considerada parte de la primera: el reconocimiento de acciones humanas en vídeos y el reconocimiento de lengua de signos.En la primera parte, la hipótesis de partida es que la transformación de las señales de un vídeo mediante el algoritmo de Patrones Espaciales Comunes (CSP por sus siglas en inglés, comúnmente utilizado en sistemas de Electroencefalografía) puede dar lugar a nuevas características que serán útiles para la posterior clasificación de los vídeos mediante clasificadores supervisados. Se han realizado diferentes experimentos en varias bases de datos, incluyendo una creada durante esta investigación desde el punto de vista de un robot humanoide, con la intención de implementar el sistema de reconocimiento desarrollado para mejorar la interacción humano-robot.En la segunda parte, las técnicas desarrolladas anteriormente se han aplicado al reconocimiento de lengua de signos, pero además de ello se propone un método basado en la descomposición de los signos para realizar el reconocimiento de los mismos, añadiendo la posibilidad de una mejor explicabilidad. El objetivo final es desarrollar un tutor de lengua de signos capaz de guiar a los usuarios en el proceso de aprendizaje, dándoles a conocer los errores que cometen y el motivo de dichos errores

Towards gestural understanding for intelligent robots

Fritsch JN. Towards gestural understanding for intelligent robots. Bielefeld: Universität Bielefeld; 2012.A strong driving force of scientific progress in the technical sciences is the quest for systems that assist humans in their daily life and make their life easier and more enjoyable. Nowadays smartphones are probably the most typical instances of such systems. Another class of systems that is getting increasing attention are intelligent robots. Instead of offering a smartphone touch screen to select actions, these systems are intended to offer a more natural human-machine interface to their users. Out of the large range of actions performed by humans, gestures performed with the hands play a very important role especially when humans interact with their direct surrounding like, e.g., pointing to an object or manipulating it. Consequently, a robot has to understand such gestures to offer an intuitive interface. Gestural understanding is, therefore, a key capability on the way to intelligent robots. This book deals with vision-based approaches for gestural understanding. Over the past two decades, this has been an intensive field of research which has resulted in a variety of algorithms to analyze human hand motions. Following a categorization of different gesture types and a review of other sensing techniques, the design of vision systems that achieve hand gesture understanding for intelligent robots is analyzed. For each of the individual algorithmic steps – hand detection, hand tracking, and trajectory-based gesture recognition – a separate Chapter introduces common techniques and algorithms and provides example methods. The resulting recognition algorithms are considering gestures in isolation and are often not sufficient for interacting with a robot who can only understand such gestures when incorporating the context like, e.g., what object was pointed at or manipulated. Going beyond a purely trajectory-based gesture recognition by incorporating context is an important prerequisite to achieve gesture understanding and is addressed explicitly in a separate Chapter of this book. Two types of context, user-provided context and situational context, are reviewed and existing approaches to incorporate context for gestural understanding are reviewed. Example approaches for both context types provide a deeper algorithmic insight into this field of research. An overview of recent robots capable of gesture recognition and understanding summarizes the currently realized human-robot interaction quality. The approaches for gesture understanding covered in this book are manually designed while humans learn to recognize gestures automatically during growing up. Promising research targeted at analyzing developmental learning in children in order to mimic this capability in technical systems is highlighted in the last Chapter completing this book as this research direction may be highly influential for creating future gesture understanding systems

Biometric Systems

Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study

State of the art of audio- and video based solutions for AAL

Working Group 3. Audio- and Video-based AAL ApplicationsIt is a matter of fact that Europe is facing more and more crucial challenges regarding health and social care due to the demographic change and the current economic context. The recent COVID-19 pandemic has stressed this situation even further, thus highlighting the need for taking action. Active and Assisted Living (AAL) technologies come as a viable approach to help facing these challenges, thanks to the high potential they have in enabling remote care and support. Broadly speaking, AAL can be referred to as the use of innovative and advanced Information and Communication Technologies to create supportive, inclusive and empowering applications and environments that enable older, impaired or frail people to live independently and stay active longer in society. AAL capitalizes on the growing pervasiveness and effectiveness of sensing and computing facilities to supply the persons in need with smart assistance, by responding to their necessities of autonomy, independence, comfort, security and safety. The application scenarios addressed by AAL are complex, due to the inherent heterogeneity of the end-user population, their living arrangements, and their physical conditions or impairment. Despite aiming at diverse goals, AAL systems should share some common characteristics. They are designed to provide support in daily life in an invisible, unobtrusive and user-friendly manner. Moreover, they are conceived to be intelligent, to be able to learn and adapt to the requirements and requests of the assisted people, and to synchronise with their specific needs. Nevertheless, to ensure the uptake of AAL in society, potential users must be willing to use AAL applications and to integrate them in their daily environments and lives. In this respect, video- and audio-based AAL applications have several advantages, in terms of unobtrusiveness and information richness. Indeed, cameras and microphones are far less obtrusive with respect to the hindrance other wearable sensors may cause to one’s activities. In addition, a single camera placed in a room can record most of the activities performed in the room, thus replacing many other non-visual sensors. Currently, video-based applications are effective in recognising and monitoring the activities, the movements, and the overall conditions of the assisted individuals as well as to assess their vital parameters (e.g., heart rate, respiratory rate). Similarly, audio sensors have the potential to become one of the most important modalities for interaction with AAL systems, as they can have a large range of sensing, do not require physical presence at a particular location and are physically intangible. Moreover, relevant information about individuals’ activities and health status can derive from processing audio signals (e.g., speech recordings). Nevertheless, as the other side of the coin, cameras and microphones are often perceived as the most intrusive technologies from the viewpoint of the privacy of the monitored individuals. This is due to the richness of the information these technologies convey and the intimate setting where they may be deployed. Solutions able to ensure privacy preservation by context and by design, as well as to ensure high legal and ethical standards are in high demand. After the review of the current state of play and the discussion in GoodBrother, we may claim that the first solutions in this direction are starting to appear in the literature. A multidisciplinary 4 debate among experts and stakeholders is paving the way towards AAL ensuring ergonomics, usability, acceptance and privacy preservation. The DIANA, PAAL, and VisuAAL projects are examples of this fresh approach. This report provides the reader with a review of the most recent advances in audio- and video-based monitoring technologies for AAL. It has been drafted as a collective effort of WG3 to supply an introduction to AAL, its evolution over time and its main functional and technological underpinnings. In this respect, the report contributes to the field with the outline of a new generation of ethical-aware AAL technologies and a proposal for a novel comprehensive taxonomy of AAL systems and applications. Moreover, the report allows non-technical readers to gather an overview of the main components of an AAL system and how these function and interact with the end-users. The report illustrates the state of the art of the most successful AAL applications and functions based on audio and video data, namely (i) lifelogging and self-monitoring, (ii) remote monitoring of vital signs, (iii) emotional state recognition, (iv) food intake monitoring, activity and behaviour recognition, (v) activity and personal assistance, (vi) gesture recognition, (vii) fall detection and prevention, (viii) mobility assessment and frailty recognition, and (ix) cognitive and motor rehabilitation. For these application scenarios, the report illustrates the state of play in terms of scientific advances, available products and research project. The open challenges are also highlighted. The report ends with an overview of the challenges, the hindrances and the opportunities posed by the uptake in real world settings of AAL technologies. In this respect, the report illustrates the current procedural and technological approaches to cope with acceptability, usability and trust in the AAL technology, by surveying strategies and approaches to co-design, to privacy preservation in video and audio data, to transparency and explainability in data processing, and to data transmission and communication. User acceptance and ethical considerations are also debated. Finally, the potentials coming from the silver economy are overviewed.publishedVersio

3D human action recognition and motion analysis using selective representations

With the advent of marker-based motion capture, attempts have been made to recognise and quantify attributes of “type”, “content” and “behaviour” from the motion data. Current work exists to obtain quick and easy identification of human motion for use in multiple settings, such as healthcare and gaming by using activity monitors, wearable technology and low-cost accelerometers. Yet, analysing human motion and generating representative features to enable recognition and analysis in an efficient and comprehensive manner has proved elusive thus far. This thesis proposes practical solutions that are based on insights from clinicians, and learning attributes from motion capture data itself. This culminates in an application framework that learns the type, content and behaviour of human motion for recognition, quantitative clinical analysis and outcome measures. While marker-based motion capture has many uses, it also has major limitations that are explored in this thesis, not least in terms of hardware costs and practical utilisation. These drawbacks have led to the creation of depth sensors capable of providing robust, accurate and low-cost solution to detecting and tracking anatomical landmarks on the human body, without physical markers. This advancement has led researchers to develop low-cost solutions to important healthcare tasks, such as human motion analysis as a clinical aid in prevention care. In this thesis a variety of obstacles in handling markerless motion capture are identified and overcome by employing parameterisation of Axis- Angles, applying Euler Angles transformations to Exponential Maps, and appropriate distance measures between postures. While developing an efficient, usable and deployable application framework for clinicians, this thesis introduces techniques to recognise, analyse and quantify human motion in the context of identifying age-related change and mobility. The central theme of this thesis is the creation of discriminative representations of the human body using novel encoding and extraction approaches usable for both marker-based and marker-less motion capture data. The encoding of the human pose is modelled based on the spatial-temporal characteristics to generate a compact, efficient parameterisation. This combination allows for the detection of multiple known and unknown motions in real-time. However, in the context of benchmarking a major drawback exists, the lack of a clinically valid and relevant dataset to enable benchmarking. Without a dataset of this type, it is difficult to validated algorithms aimed at healthcare application. To this end, this thesis introduces a dataset that will enable the computer science community to benchmark healthcare-related algorithms

Biometric Systems

Biometric authentication has been widely used for access control and security systems over the past few years. The purpose of this book is to provide the readers with life cycle of different biometric authentication systems from their design and development to qualification and final application. The major systems discussed in this book include fingerprint identification, face recognition, iris segmentation and classification, signature verification and other miscellaneous systems which describe management policies of biometrics, reliability measures, pressure based typing and signature verification, bio-chemical systems and behavioral characteristics. In summary, this book provides the students and the researchers with different approaches to develop biometric authentication systems and at the same time includes state-of-the-art approaches in their design and development. The approaches have been thoroughly tested on standard databases and in real world applications